灰水足迹视角下我国省域农业生态效率及其影响因素

doi:10.3864/j.issn.0578-1752.2022.24.008

Abstract

Abstract:

【Objective】This paper evaluated Chinese provincial agricultural ecological efficiency from the perspective of gray water footprint, revealed the spatial distribution characteristics of agricultural ecological efficiency, analyzed the main factors affecting agricultural ecological efficiency, and put forward policy suggestions to improve Chinese provincial agricultural ecological efficiency. 【Method】Based on the provincial panel data of China from 2000 to 2019, this paper comprehensively evaluated the agricultural ecological efficiency of Chinese provinces with the super efficiency SBM model considering the unexpected output, and used the spatial Dobbin model to analyze the spatial differences and influencing factors of agricultural ecological efficiency. 【Result】(1) In general, the agricultural grey water footprint showed a downward trend, but in some provinces (cities and districts), it showed an upward trend. From the ranking of grey water footprint from low to high, it could be seen that the provinces (cities and districts) in the forefront (i.e. with less grey water footprint) had a high level of economic development or a relatively low proportion of agricultural output value; the provinces (cities and districts) in the rear row (i.e. with more grey water footprint) had low economic development level or high agricultural output value. (2) During the observation period, the agricultural ecological efficiency fluctuated greatly in some years in the stable trend, and the average difference among provinces (cities and districts) was obvious and the distribution was extremely unbalanced. (3) Economic development level, fiscal expenditure for supporting agriculture, technological progress, agricultural disaster rate, planting structure and other factors had different impacts on Chinese agricultural ecological efficiency. With the improvement of both economic development level and people's living quality, both agricultural operators and consumers paid more attention to the protection of agricultural ecological environment and the quality of agricultural products, which have improved the level of regional agricultural ecological efficiency to a certain extent. But the pollution caused by regional economic and social development might also have a negative impact on agricultural ecological efficiency. Most of the financial support for agriculture was used to subsidize production links, such as pesticides, chemical fertilizers, and agricultural machinery. Although the agricultural production conditions have been improved and the agricultural economic productivity and efficiency have been improved, the improvement of agricultural ecological efficiency was not significant. The development of technology was very important in the agricultural production process, and the proper use of it would improve the agricultural ecological efficiency. The estimated results of agricultural disaster rate failed to pass the significance test, which might be because the expansion of agricultural disaster area would lead to the decline of agricultural ecological efficiency, but the annual disaster situation was not regular. The coefficient of planting structure was negative, which had a negative impact on agricultural production efficiency. This might be due to the high proportion of grain crop planting area in the total planting area of crops, and the high consumption of nitrogen fertilizer. 【Conclusion】As the evolution trend and difference of agricultural gray water footprint in various provinces (cities and districts) in China were obvious, the overall level of agricultural ecological efficiency was not high, and various factors have different impacts on agricultural ecological efficiency, it was necessary to improve the governance mechanism of agricultural gray water footprint; optimize the agricultural industrial structure and establish a compensation mechanism for agricultural water resources protection based on gray water footprint; improve the ways and policies of financial support for agriculture, and guide business entities to actively improve agricultural ecological efficiency.

Key words: agricultural ecological efficiency, agricultural grey water footprint, super-SBM model

DENG YuanJian,CHAO Bo. Provincial Agricultural Ecological Efficiency and Its Influencing Factors in China from the Perspective of Grey Water Footprint[J].Scientia Agricultura Sinica, 2022, 55(24): 4879-4894.

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Figures/Tables 12

Table 1

Fig. 1

Table 2

Table 3

Measurement results of agricultural ecological efficiency of 30 provinces (cities and districts) in China from 2000 to 2019"

省域 Province	年份 Year
省域 Province	2000	2002	2004	2006	2008	2010	2012	2014	2016	2018	2019
北京 Beijing	1.069	1.171	1.271	1.208	1.135	1.124	1.088	1.130	1.165	1.664	1.696
天津Tianjin	1.392	1.315	1.160	1.198	1.208	1.149	1.147	1.136	1.090	1.014	1.023
河北 Hebei	1.286	1.206	1.241	1.240	1.255	1.149	1.108	1.094	1.004	0.176	1.027
山西 Shanxi	0.723	1.003	1.024	1.056	1.000	1.013	0.845	1.059	0.578	0.167	0.469
内蒙古 Neimenggu	0.488	0.504	0.532	0.549	1.046	1.035	1.026	1.046	1.027	0.216	1.031
辽宁 Liaoning	0.426	0.451	0.468	0.532	0.514	0.536	0.533	0.573	0.588	0.162	0.574
吉林 Jilin	0.385	0.384	0.403	0.454	0.484	0.560	0.595	0.597	1.050	0.231	1.162
黑龙江 Heilongjiang	0.447	0.438	0.450	0.521	0.542	1.002	1.042	1.004	1.111	0.176	1.126
上海 Shanghai	1.062	1.037	0.844	1.102	1.136	1.257	1.320	1.155	1.037	1.025	1.026
江苏 Jiangsu	0.416	0.406	0.400	0.410	0.429	0.452	0.463	0.507	0.709	0.144	0.694
浙江 Zhejiang	1.023	1.007	0.681	1.001	0.677	0.681	0.668	0.665	0.746	4.036	0.650
安徽 Anhui	0.527	0.559	0.584	0.622	0.605	0.700	0.717	0.677	1.080	0.173	1.024
福建 Fujian	0.376	0.393	0.416	0.400	0.422	0.432	0.459	0.463	0.519	0.179	0.481
江西 Jiangxi	0.279	0.336	0.433	0.594	1.002	1.093	1.106	0.445	0.567	0.157	0.620
山东 Shandong	1.008	1.027	1.019	1.022	1.003	1.016	1.020	1.026	1.110	0.170	1.112
河南 Henan	0.631	0.648	0.685	0.689	0.712	0.705	0.728	0.750	1.024	0.147	0.831
湖北 Hubei	0.315	0.337	0.347	0.422	0.450	0.509	0.532	0.562	0.662	0.144	0.667
湖南 Hunan	0.449	0.475	0.506	0.557	0.598	0.641	0.674	0.705	1.081	0.185	1.031
广东 Guangdong	0.463	0.402	0.398	0.376	0.374	0.405	0.421	0.440	0.489	0.120	0.474
广西 Guangxi	0.422	0.440	0.449	0.413	0.450	0.502	0.540	0.568	1.018	0.154	1.014
海南 Hainan	1.089	1.005	1.012	1.008	1.015	1.017	1.030	0.759	1.039	0.456	1.066
重庆 Chongqing	0.304	0.349	0.388	0.403	0.404	0.445	0.444	0.475	0.596	0.214	0.622
四川 Sichuan	0.296	0.312	0.331	0.370	0.348	0.426	0.462	0.447	0.586	0.117	0.595
贵州 Guizhou	0.292	0.332	0.385	0.542	0.520	0.613	0.537	0.620	0.601	0.170	1.007
云南 Yunnan	0.357	0.371	0.387	0.393	0.417	0.502	0.491	0.492	0.674	0.123	0.471
陕西 Shaanxi	0.342	0.374	0.405	0.424	0.445	0.486	0.524	0.543	0.583	0.160	0.577
甘肃 Gansu	0.484	0.510	0.539	0.533	0.555	0.616	0.660	0.663	0.646	0.192	0.673
青海 Qinghai	1.868	1.773	1.588	1.702	1.503	1.397	1.598	1.054	1.024	1.035	1.061
宁夏 Ningxia	0.682	1.002	1.005	1.012	0.805	1.007	1.033	1.026	0.776	1.007	1.014
新疆 Xinjiang	0.357	0.371	0.383	0.403	0.415	0.508	0.528	0.369	0.503	0.144	0.485

Table 3

Table 4

Fig. 2

Table 5

Table 6

Table 7

Table 8

Table 9

Table 10

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指标类型 Index type	指标类别 Index category	指标名称 Index name	指标说明 Index description
投入指标 Input index	资源类指标 Resource index	劳动力投入 Labor input	农林牧渔业从业人员（万人） Agricultural, forestry, animal husbandry and fishery employees (ten thousand people)
		土地投入 Land input	农作物播种面积 Planting area of crops (×10³hm²)
		化肥投入 Chemical fertilizer input	农用化肥施用折纯量 Net amount of agricultural chemical fertilizer application (×10⁴t)
		农业机械投入 Agricultural machinery input	农业机械总动力 Total power of agricultural machinery (×10⁴kW·h)
		灌溉投入 Irrigation input	有效灌溉面积 Effective irrigation area (hm²)
		农膜投入 Agricultural film input	农膜使用量 Amounts of agricultural film used (×10⁴ t)
		农药投入 Pesticide input	农药使用量 Amounts of pesticides used (×10⁴ t)
期望产出指标 Expected output index	经济类指标 Economic index	农业生产总值 Gross agricultural production	农林牧渔业生产总值 Gross output value of agriculture, forestry, animal husbandry and fishery (×10⁸ yuan)
非期望产出指标 Unexpected output index	环境类指标 Environmental index	农业灰水足迹 Agricultural grey water footprint	稀释农业生产活动排放的一定量的水污染物所需要的自然水体体积 Volume of natural water body required to dilute a certain amount of water pollutants discharged from agricultural production activities (m³)

省域 Province	均值与排名 Average value and ranking
	2000—2004年 From 2000 to 2004		2005—2009年 From 2005 to 2009		2010—2014年 From 2010 to 2014		2015—2019年 From 2015 to 2019
	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)	排名 Ranking	均值 Average value (m³)
青海 Qinghai	1	3.2	1	3.3	1	3.9	1	3.3
北京 Beijing	2	8.5	2	7.2	3	5.9	2	3.4
天津 Tianjin	3	10.1	4	12.2	4	10.9	4	6.6
海南 Hainan	4	10.9	5	13.1	5	14.5	5	14.9
上海 Shanghai	5	11.7	3	7.7	2	5.4	3	3.9
宁夏 Ningxia	6	14.1	6	16.6	6	17.9	6	16.8
甘肃 Gansu	7	33.8	7	37.7	8	39.8	8	34.4
山西 Shanxi	8	40.7	8	40.1	7	37.2	7	26.9
贵州 Guizhou	9	43.4	10	46.3	13	51.8	11	43.3
重庆 Chongqing	10	45.5	12	48.8	12	49.8	13	46.4
新疆 Xinjiang	11	45.9	18	69.2	20	96.4	24	110.4
江西 Jiangxi	12	46.9	9	44.6	9	42.8	9	35.9
内蒙古 Neimenggu	13	49.8	19	73.1	18	89.6	19	90.7
福建 Fujian	14	52.3	11	48.3	10	47.3	12	43.4
黑龙江 Heilongjiang	15	53.7	17	68.7	17	86.4	17	82.3
浙江 Zhejiang	16	56.5	13	53.7	11	49.4	10	40.7
广西 Guangxi	17	59.5	16	67.8	16	73.3	16	74.4
辽宁 Liaoning	18	64.2	15	65.7	14	68.3	14	55.8
吉林 Jilin	19	66.2	14	64.3	15	71.3	15	62.5
云南 Yunnan	20	73.6	21	90.3	23	109.8	23	108.1
陕西 Shaanxi	21	74.8	20	82.7	19	95.6	18	87.9
广东 Guangdong	22	96.2	22	97.2	21	102.1	21	95.8
湖南 Hunan	23	100.3	23	108.1	22	108.6	20	94.1
安徽 Anhui	24	117.7	24	111.9	24	112.3	22	97.3
四川 Sichuan	25	120.5	25	128.3	25	126.6	25	113.7
湖北 Hubei	26	135.6	26	148.5	27	151.2	26	119.9
河北 Hebei	27	149.3	27	154.1	26	150.6	27	126.6
江苏 Jiangsu	28	185.5	28	176.9	29	166.9	29	149.1
山东 Shandong	29	190.5	29	181.5	28	156.3	28	133.7
河南 Henan	30	216.9	30	239.4	30	242.9	30	209.9

年份Year	莫兰值Morans’I	P值P value	Z值Z value	年份Year	莫兰值Morans’I	P值P value	Z值Z value
2000	0.135	0.009	2.387	2010	0.204	0.001	3.210
2001	0.133	0.006	2.496	2011	0.227	0.000	3.519
2002	0.151	0.005	2.577	2012	0.211	0.000	3.365
2003	0.197	0.001	3.176	2013	0.154	0.006	2.536
2004	0.191	0.001	3.110	2014	0.179	0.002	2.868
2005	0.175	0.002	2.879	2015	0.191	0.001	3.028
2006	0.154	0.005	2.610	2016	0.075	0.071	1.465
2007	0.169	0.003	2.792	2017	0.034	0.164	0.980
2008	0.185	0.001	2.981	2018	0.012	0.329	0.433
2009	0.206	0.001	3.282	2019	0.132	0.010	2.310

因变量 Dependent variable	符号 Symbol	变量解释 Interpretation of variables
人均收入 Per capita income	RCI	农村居民人均可支配收入 Per capita disposable income of rural residents
财政支农支出 Fiscal expenditure on supporting agriculture	AFI	地方财政农林水事务支出/地方财政一般预算支出 Amount of agricultural, forestry and water affairs expenditure in local fiscal expenditure/ General budget expenditure of local finance (%)
技术水平 Technical level	TI	R&D投入/地方财政一般预算支出 R&D input/ General budget expenditure of local finance (%)
经济发展水平 Economic development level	LED	地区人均GDP Regional per capita GDP
农业受灾率 Agricultural disaster rate	RDR	农作物受灾面积/农作物总播种面积 Area affected by crops/ Total sown area of crops (%)
种植业结构 Planting structure	CS	粮食作物种植面积/农作物总播种面积 Grain crop planting area/ Total sown area of crops (%)

变量名称 Variable name	均值 Average value	标准差 Standard deviation	最小值 Minimum	最大值 Maximum	样本数 Number of samples
ECOE	0.69	0.38	0.11	4.04	600
RCI	7.8e	7.4e	1.41	1.3e	600
AFI	274.95	279.87	2.14	1.3e	600
TI	6.5e	7.4e	80.91	4.4e	600
LED	0.92	0.46	0.05	2.71	600
RDR	0.23	0.16	0.00	0.94	600
CS	0.53	0.09	0.34	0.75	600

Provincial Agricultural Ecological Efficiency and Its Influencing Factors in China from the Perspective of Grey Water Footprint

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省域 Province	平均值 Average value	排序 Ranking	省域 Province	平均值 Average value	排序 Ranking
北京 Beijing	1.2263	1	甘肃 Gansu	0.5466	16
天津 Tianjin	1.1727	2	吉林 Jilin	0.5260	17
上海 Shanghai	1.0948	3	广西 Guangxi	0.5009	18
河北 Hebei	1.0699	4	贵州 Guizhou	0.4984	19
宁夏 Ningxia	0.9501	5	辽宁 Liaoning	0.4831	20
山东 Shandong	0.9433	6	青海 Qinghai	0.4423	21
海南 Hainan	0.9273	7	江苏 Jiangsu	0.4355	22
浙江 Zhejiang	0.9058	8	陕西 Shaanxi	0.4345	23
山西 Shanxi	0.8491	9	湖北 Hubei	0.4329	24
内蒙古 Neimenggu	0.7482	10	云南 Yunnan	0.4137	25
河南 Henan	0.7032	11	重庆 Chongqing	0.4096	26
黑龙江 Heilongjiang	0.6637	12	福建 Fujian	0.4084	27
安徽 Anhui	0.6268	13	新疆 Xinjiang	0.3917	28
湖南 Hunan	0.5907	14	广东 Guangdong	0.3914	29
江西 Jiangxi	0.5871	15	四川 Sichuan	0.3779	30

年份 Year	高-高集群 High-High cluster	高-低集群 High-Low cluster	低-低集群 Low-Low cluster	低异常值 Low outlier
2000	北京、天津、河北 Beijing, Tianjin, Hebei	青海 Qinghai	贵州、重庆 Guizhou, Chongqing	新疆 Xinjiang
2004	北京、天津、河北 Beijing, Tianjin, Hebei	青海、海南 Qinghai, Hainan		新疆 Xinjiang
2009	内蒙古、北京、天津、河北 Neimenggu, Beijing,Tianjin, Hebei	青海 Qinghai	重庆 Chongqing	新疆 Xinjiang
2014	内蒙古、北京、天津、河北、山东、山西 Neimenggu, Beijing, Tianjin, Hebei, Shandong, Shanxi		广东、重庆 Guangdong, Chongqing	辽宁 Liaoning
2019	北京 Beijing			辽宁 Liaoning

	ECOE (1)	ECOE (2)	ECOE (3)	ECOE (4)
RCI_it	-0.242*** (-2.741)	-0.325*** (-2.735)	-0.317** (-2.352)	-0.296** (-2.173)
RCI²_it	2.351*** (4.812)	1.869*** (3.551)	1.662*** (3.129)	1.386*** (2.875)
LED_it			-4.18* (-2.13)	0.000* (0.263)
CS_it			-0.065* (-2.04)	4.189* (1.493)
AFI_it			0.002** (2.346)	-0.001** (-2.097)
TI_it			3.88e** (0.001)	0.179** (2.013)
RDR_it			-0.085 (-1.02)	-6.251 (-3.276)
_cons	-0.233** (-2.152)	-0.128** (-2.637)	0.318* (1.851)	0.394* (1.727)
R²	0.166	0.168	0.163	0.167
控制时间Control time	不控制Out of control	不控制Out of control	不控制Out of control	控制control
控制省份Control the provinces	不控制Out of control	控制control	不控制Out of control	控制control
样本数 Number of samples	600	600	600	600
拐点 Inflection point	0.593	0.577	0.561	0.558

区域 Region	解释变量 Explanatory variable	系数 Coefficient	区域 Region	解释变量 Explanatory variable	系数 Coefficient
东北地区 Northeast region	RCI_it	-0.103*	中部地区 Central region	RCI_it	-0.034**
	RCI_it²	2.108**		RCI_it²	1.552*
	LED_it	-0.005**		LED_it	-0.213***
	CS_it	-0.461**		CS_it	-0.245*
	AFI_it	-0.121*		AFI_it	-0.314*
	TI_it	0.231**		TI_it	0.125**
	RDR_it	-0.424		RDR_it	-0.081
东部地区 Eastern region	RCI_it	-0.191*	西部地区 Western region	RCI_it	-0.112*
	RCI_it²	1.976*		RCI_it²	1.495**
	LED_it	-0.001**		LED_it	-0.006**
	CS_it	-0.524**		CS_it	-0.032**
	AFI_it	-0.137**		AFI_it	0.046**
	TI_it	0.172*		TI_it	0.087*
	RDR_it	-0.315		RDR_it	-0.631